الفكاهة والتصنيف يشكل تحديات لغوية مثيرة للاهتمام إلى NLP؛إنها ذاتية عالية اعتمادا على تصورات مزحة والسياق الذي يستخدم فيه.تستخدم هذه الورقة ويقارن نماذج المحولات؛Bert Base و Large، Bertweet، Roberta Base and Large، مفارقة قاعدة روبرتا، للكشف عن الفكاهة والفكاهة.النماذج المقترحة، حيث نظمت نصا في نوع غلاف وغير مقصود تم الحصول عليها من مهمة Semeval-2021: hahackathon: ربط الفكاهة والجريمة عبر الفئات العمرية المختلفة.أعلى نموذج مسجل في المراكب الفرعي الأول: الكشف عن الفكاهة، نموذج Bertweet Base CaseD مع 0.9540 F1-Score، للمرجع الفرعي الثاني: متوسط درجة التصنيف الفكاهي، فهو Bert Large Cased مع الحد الأدنى من RMSE من 0.5555، في المراكز الفرعية الرابعة:متوسط درجة تصنيف الاكتشاف، إنها نموذج Bertweet Base Cased مع الحد الأدنى من RMSE من 0.4822.
Humor detection and rating poses interesting linguistic challenges to NLP; it is highly subjective depending on the perceptions of a joke and the context in which it is used. This paper utilizes and compares transformers models; BERT base and Large, BERTweet, RoBERTa base and Large, and RoBERTa base irony, for detecting and rating humor and offense. The proposed models, where given a text in cased and uncased type obtained from SemEval-2021 Task7: HaHackathon: Linking Humor and Offense Across Different Age Groups. The highest scored model for the first subtask: Humor Detection, is BERTweet base cased model with 0.9540 F1-score, for the second subtask: Average Humor Rating Score, it is BERT Large cased with the minimum RMSE of 0.5555, for the fourth subtask: Average Offensiveness Rating Score, it is BERTweet base cased model with minimum RMSE of 0.4822.
References used
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